Benchling Integration

Overview

Benchling is a cloud platform for life sciences R&D. Access registry entities (DNA, proteins), inventory, electronic lab notebooks, and workflows programmatically via Python SDK and REST API.

When to Use This Skill

This skill should be used when:

• Working with Benchling's Python SDK or REST API
• Managing biological sequences (DNA, RNA, proteins) and registry entities
• Automating inventory operations (samples, containers, locations, transfers)
• Creating or querying electronic lab notebook entries
• Building workflow automations or Benchling Apps
• Syncing data between Benchling and external systems
• Querying the Benchling Data Warehouse for analytics
• Setting up event-driven integrations with AWS EventBridge

Core Capabilities

1. Authentication & Setup

Python SDK Installation:

# Stable release
uv pip install benchling-sdk
# or with Poetry
poetry add benchling-sdk

Authentication Methods:

API Key Authentication (recommended for scripts):

from benchling_sdk.benchling import Benchling
from benchling_sdk.auth.api_key_auth import ApiKeyAuth

benchling = Benchling(
    url="https://your-tenant.benchling.com",
    auth_method=ApiKeyAuth("your_api_key")
)

OAuth Client Credentials (for apps):

from benchling_sdk.auth.client_credentials_oauth2 import ClientCredentialsOAuth2

auth_method = ClientCredentialsOAuth2(
    client_id="your_client_id",
    client_secret="your_client_secret"
)
benchling = Benchling(
    url="https://your-tenant.benchling.com",
    auth_method=auth_method
)

Key Points:

• API keys are obtained from Profile Settings in Benchling
• Store credentials securely (use environment variables or password managers)
• All API requests require HTTPS
• Authentication permissions mirror user permissions in the UI

For detailed authentication information including OIDC and security best practices, refer to references/authentication.md.

2. Registry & Entity Management

Registry entities include DNA sequences, RNA sequences, AA sequences, custom entities, and mixtures. The SDK provides typed classes for creating and managing these entities.

Creating DNA Sequences:

from benchling_sdk.models import DnaSequenceCreate

sequence = benchling.dna_sequences.create(
    DnaSequenceCreate(
        name="My Plasmid",
        bases="ATCGATCG",
        is_circular=True,
        folder_id="fld_abc123",
        schema_id="ts_abc123",  # optional
        fields=benchling.models.fields({"gene_name": "GFP"})
    )
)

Registry Registration:

To register an entity directly upon creation:

sequence = benchling.dna_sequences.create(
    DnaSequenceCreate(
        name="My Plasmid",
        bases="ATCGATCG",
        is_circular=True,
        folder_id="fld_abc123",
        entity_registry_id="src_abc123",  # Registry to register in
        naming_strategy="NEW_IDS"  # or "IDS_FROM_NAMES"
    )
)

Important: Use either entity_registry_id OR naming_strategy, never both.

Updating Entities:

from benchling_sdk.models import DnaSequenceUpdate

updated = benchling.dna_sequences.update(
    sequence_id="seq_abc123",
    dna_sequence=DnaSequenceUpdate(
        name="Updated Plasmid Name",
        fields=benchling.models.fields({"gene_name": "mCherry"})
    )
)

Unspecified fields remain unchanged, allowing partial updates.

Listing and Pagination:

# List all DNA sequences (returns a generator)
sequences = benchling.dna_sequences.list()
for page in sequences:
    for seq in page:
        print(f"{seq.name} ({seq.id})")

# Check total count
total = sequences.estimated_count()

Key Operations:

• Create: benchling.<entity_type>.create()
• Read: benchling.<entity_type>.get(id) or .list()
• Update: benchling.<entity_type>.update(id, update_object)
• Archive: benchling.<entity_type>.archive(id)

Entity types: dna_sequences, rna_sequences, aa_sequences, custom_entities, mixtures

For comprehensive SDK reference and advanced patterns, refer to references/sdk_reference.md.

3. Inventory Management

Manage physical samples, containers, boxes, and locations within the Benchling inventory system.

Creating Containers:

from benchling_sdk.models import ContainerCreate

container = benchling.containers.create(
    ContainerCreate(
        name="Sample Tube 001",
        schema_id="cont_schema_abc123",
        parent_storage_id="box_abc123",  # optional
        fields=benchling.models.fields({"concentration": "100 ng/μL"})
    )
)

Managing Boxes:

from benchling_sdk.models import BoxCreate

box = benchling.boxes.create(
    BoxCreate(
        name="Freezer Box A1",
        schema_id="box_schema_abc123",
        parent_storage_id="loc_abc123"
    )
)

Transferring Items:

# Transfer a container to a new location
transfer = benchling.containers.transfer(
    container_id="cont_abc123",
    destination_id="box_xyz789"
)

Key Inventory Operations:

• Create containers, boxes, locations, plates
• Update inventory item properties
• Transfer items between locations
• Check in/out items
• Batch operations for bulk transfers

4. Notebook & Documentation

Interact with electronic lab notebook (ELN) entries, protocols, and templates.

Creating Notebook Entries:

from benchling_sdk.models import EntryCreate

entry = benchling.entries.create(
    EntryCreate(
        name="Experiment 2025-10-20",
        folder_id="fld_abc123",
        schema_id="entry_schema_abc123",
        fields=benchling.models.fields({"objective": "Test gene expression"})
    )
)

Linking Entities to Entries:

# Add references to entities in an entry
entry_link = benchling.entry_links.create(
    entry_id="entry_abc123",
    entity_id="seq_xyz789"
)

Key Notebook Operations:

• Create and update lab notebook entries
• Manage entry templates
• Link entities and results to entries
• Export entries for documentation

5. Workflows & Automation

Automate laboratory processes using Benchling's workflow system.

Creating Workflow Tasks:

from benchling_sdk.models import WorkflowTaskCreate

task = benchling.workflow_tasks.create(
    WorkflowTaskCreate(
        name="PCR Amplification",
        workflow_id="wf_abc123",
        assignee_id="user_abc123",
        fields=benchling.models.fields({"template": "seq_abc123"})
    )
)

Updating Task Status:

from benchling_sdk.models import WorkflowTaskUpdate

updated_task = benchling.workflow_tasks.update(
    task_id="task_abc123",
    workflow_task=WorkflowTaskUpdate(
        status_id="status_complete_abc123"
    )
)

Asynchronous Operations:

Some operations are asynchronous and return tasks:

# Wait for task completion